Functional Profiling - Usage Guide

Functional Profiling - Usage Guide

Overview

HUMAnN3 profiles the functional potential of metagenomic communities by quantifying gene families (UniRef90) and inferring pathway abundances (MetaCyc).

Prerequisites

conda create -n humann -c bioconda humann
conda activate humann

# Download databases (~16 GB total)
humann_databases --download chocophlan full /db/humann
humann_databases --download uniref uniref90_diamond /db/humann

Quick Start

Tell your AI agent what you want to do:

• "Profile the functional potential of my metagenome"
• "Identify metabolic pathways in my microbial community"
• "Compare pathway abundances across treatment groups"

Example Prompts

Basic Profiling

"Run HUMAnN3 on sample.fastq.gz to get pathway abundances"

"Profile functional genes in my metagenome with 8 threads"

Multi-sample Analysis

"Process all fastq files through HUMAnN3 and merge the results"

"Normalize pathway abundances to relative abundance and compare groups"

Gene Family Analysis

"Regroup gene families to KEGG Orthologs (KO)"

"Show which species contribute to each pathway in my sample"

Database Selection

"Use the smaller uniref50 database for faster processing"

"Run HUMAnN3 with a pre-computed MetaPhlAn profile to speed things up"

What the Agent Will Do

1. Run MetaPhlAn for taxonomic profiling (if not provided)
2. Map reads to species-specific pangenomes
3. Translate unmapped reads to protein database
4. Quantify gene families and infer pathway abundances
5. Merge and normalize results across samples if requested

Tips

• Concatenate paired-end reads before running (HUMAnN handles both orientations)
• Pre-compute MetaPhlAn profile with --taxonomic-profile for speed
• High UNMAPPED rate indicates missing database coverage
• Always normalize before cross-sample comparison (use humann_renorm_table)
• Stratified output shows species contributions to each pathway

Database Options

Key Parameters

Resources

• HUMAnN3 User Manual
• bioBakery Tools